Document Open Access Logo

Improved De Novo Peptide Sequencing using LC Retention Time Information

Authors Yves Frank, Tomas Hruz, Thomas Tschager, Valentin Venzin



PDF
Thumbnail PDF

File

LIPIcs.WABI.2017.26.pdf
  • Filesize: 0.64 MB
  • 17 pages

Document Identifiers

Author Details

Yves Frank
Tomas Hruz
Thomas Tschager
Valentin Venzin

Cite AsGet BibTex

Yves Frank, Tomas Hruz, Thomas Tschager, and Valentin Venzin. Improved De Novo Peptide Sequencing using LC Retention Time Information. In 17th International Workshop on Algorithms in Bioinformatics (WABI 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 88, pp. 26:1-26:17, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2017)
https://doi.org/10.4230/LIPIcs.WABI.2017.26

Abstract

Liquid chromatography combined with tandem mass spectrometry (LC-MS/MS) is an important tool in proteomics for identifying the peptides in a sample. Liquid chromatography temporally separates the peptides and tandem mass spectrometry analyzes the peptides, that elute one after another, by measuring their mass-to-charge ratios and the mass-to-charge ratios of their prefix and suffix fragments. De novo peptide sequencing is the problem of reconstructing the amino acid sequences of the analyzed peptide from this measurement data. While previous approaches solely consider the mass spectrum of the fragments for reconstructing a sequence, we propose to also exploit the information obtained from liquid chromatography. We study the problem of computing a sequence that is not only in accordance with the experimental mass spectrum, but also with the retention time of the separation by liquid chromatography. We consider three models for predicting the retention time of a peptide and develop algorithms for de novo sequencing for each model. An evaluation on experimental data from synthesized peptides for two of these models shows an improved performance compared to not using the chromatographic information.
Keywords
  • Computational proteomics
  • Peptide identification
  • Mass spectrometry
  • De novo peptide sequencing
  • Retention time prediction

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads

References

  1. Ting Chen, Ming-Yang Kao, Matthew Tepel, John Rush, and George M. Church. A dynamic programming approach to de novo peptide sequencing via tandem mass spectrometry. Journal of Computational Biology, 8(3):325-337, 2001. URL: http://dx.doi.org/10.1089/10665270152530872.
  2. Vlado Dančík, Theresa A. Addona, Karl R. Clauser, James E. Vath, and Pavel A. Pevzner. De novo peptide sequencing via tandem mass spectrometry. Journal of Computational Biology, 6(3-4):327-342, 1999. URL: http://dx.doi.org/10.1089/106652799318300.
  3. Jimmy K. Eng, Tahmina A. Jahan, and Michael R. Hoopmann. Comet: an open-source MS/MS sequence database search tool. Proteomics, 13(1):22-24, 2013. URL: http://dx.doi.org/10.1002/pmic.201200439.
  4. Ludovic Gillet, Simon Rösch, Thomas Tschager, and Peter Widmayer. A better scoring model for de novo peptide sequencing: The symmetric difference between explained and measured masses. In 16th International Workshop on Algorithms in Bioinformatics, WABI 2016, volume 9838, pages 185-196, 2016. (extended version: [17]). URL: http://dx.doi.org/10.1007/978-3-319-43681-4.
  5. Christopher Hughes, Bin Ma, and Gilles A. Lajoie. De novo sequencing methods in proteomics. Proteome Bioinformatics, 604:105-121, 2010. URL: http://dx.doi.org/10.1007/978-1-60761-444-9_8.
  6. Kyowon Jeong, Sangtae Kim, and Pavel A. Pevzner. UniNovo: a universal tool for de novo peptide sequencing. Bioinformatics (Oxford, England), 29(16):1953-1962, 2013. URL: http://dx.doi.org/10.1093/bioinformatics/btt338.
  7. Michael Kinter and Nicholas E. Sherman. Protein Sequencing and Identification Using Tandem Mass Spectrometry. Wiley-Interscience, New York, 2000. URL: http://dx.doi.org/10.1002/0471721980.
  8. Oleg V. Krokhin. Sequence-specific retention calculator. Algorithm for peptide retention prediction in ion-pair RP-HPLC: Application to 300- and 100-A pore size C18 sorbents. Analytical chemistry, 78(22):7785-95, 2006. URL: http://dx.doi.org/10.1021/ac060777w.
  9. Oleg. V Krokhin, Robertson Craig, Vic Spicer, Werner Ens, Kenneth G. Standing, Ronald C. Beavis, and John A. Wilkins. An improved model for prediction of retention times of tryptic peptides in ion pair reversed-phase HPLC: its application to protein peptide mapping by off-line HPLC-MALDI MS. Molecular &cellular proteomics : MCP, 3(9):908-19, 2004. URL: http://dx.doi.org/10.1074/mcp.M400031-MCP200.
  10. Bin Ma. Novor: Real-time peptide de novo sequencing software. Journal of The American Society for Mass Spectrometry, 26(11):1885-1894, 2015. URL: http://dx.doi.org/10.1007/s13361-015-1204-0.
  11. Luminita Moruz and Lukas Käll. Peptide retention time prediction. Mass spectrometry reviews, 2016. URL: http://dx.doi.org/10.1002/mas.21488.
  12. Magnus Palmblad, Margareta Ramström, Karin E. Markides, Per Håkansson, and Jonas Bergquist. Prediction of chromatographic retention and protein identification in liquid chromatography/mass spectrometry. Analytical Chemistry, 74(22):5826-5830, 2002. URL: http://dx.doi.org/10.1021/ac0256890.
  13. Hannes L Röst, George Rosenberger, Pedro Navarro, Ludovic Gillet, Saša M. Miladinović, Olga T. Schubert, Witold Wolski, Ben C Collins, Johan Malmström, Lars Malmström, and Ruedi Aebersold. OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nature biotechnology, 32(3):219-223, 2014. URL: http://dx.doi.org/10.1038/nbt.2841.
  14. Kosaku Shinoda, Masahiro Sugimoto, Masaru Tomita, and Yasushi Ishihama. Informatics for peptide retention properties in proteomic LC-MS. Proteomics, 8(4):787-98, 2008. URL: http://dx.doi.org/10.1002/pmic.200700692.
  15. Vic Spicer, Marine Grigoryan, Alexander Gotfrid, Kenneth G. Standing, and Oleg V. Krokhin. Predicting retention time shifts associated with variation of the gradient slope in peptide RP-HPLC. Analytical chemistry, 82(23):9678-85, 2010. URL: http://dx.doi.org/10.1021/ac102228a.
  16. Eric F. Strittmatter, Lars J. Kangas, Konstantinos Petritis, Heather M. Mottaz, Gordon A. Anderson, Yufeng Shen, Jon M. Jacobs, David G. Camp, and Richard D. Smith. Application of peptide LC retention time information in a discriminant function for peptide identification by tandem mass spectrometry. Journal of Proteome Research, 3(4):760-769, 2004. URL: http://dx.doi.org/10.1021/pr049965y.
  17. Thomas Tschager, Simon Rösch, Ludovic Gillet, and Peter Widmayer. A better scoring model for de novo peptide sequencing: The symmetric difference between explained and measured masses. Algorithms for Molecular Biology, 12(1), 2017. (extended version of [4]). URL: http://dx.doi.org/10.1186/s13015-017-0104-1.
  18. Susan K. Van Riper, Ebbing P. de Jong, John V. Carlis, and Timothy J Griffin. Mass spectrometry-based proteomics: Basic principles and emerging technologies and directions. Advances in experimental medicine and biology, 990:1-35, 2013. URL: http://dx.doi.org/10.1007/978-94-007-5896-4_1.
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail